Multi-antenna techniques can significantly increase the data rates and reliability of a wireless communication system. The performance is in particular improved if both the transmitter and the receiver are equipped with multiple antennas, which results in a multiple-input multiple-output (MIMO) communication channel. Such systems and/or related techniques are commonly referred to as MIMO.
The LTE standard is currently evolving with enhanced MIMO support. A core component in LTE is the support of MIMO antenna deployments and MIMO related techniques. A current working assumption in LTE-Advanced is the support of an 8-layer spatial multiplexing mode with possibly channel dependent precoding. The spatial multiplexing mode is intended for high data rates in favorable channel conditions. According to such multiplexing, an information carrying symbol vector s is multiplied by an NT×r precoder matrix WNT×r, which serves to distribute the transmit energy in a subspace of the NT (corresponding to NT antenna ports) dimensional vector space.
The precoder matrix is typically selected from a codebook of possible precoder matrices, and is typically indicated by means of a precoder matrix indicator (PMI), which specifies a unique precoder matrix in the codebook. If the precoder matrix is confined to have orthonormal columns, then the design of the codebook of precoder matrices corresponds to a Grassmannian subspace-packing problem. The r symbols in s each correspond to a layer and r is referred to as the transmission rank. In this way, spatial multiplexing is achieved since multiple symbols can be transmitted simultaneously over the same resource element (RE). The number of symbols r is typically adapted to suit the current channel properties.
LTE uses OFDM in the downlink (and DFT precoded OFDM in the uplink) and hence the received NR×1 vector yn for a certain resource element on subcarrier n (or alternatively data RE number n), assuming no inter-cell interference, is thus modeled byyn=HnWNT×rsn+en where en is a noise vector obtained as realizations of a random process. The precoder, WNT×r, can be a wideband precoder, which is constant over frequency, or frequency selective. The precoder matrix is often chosen to match the characteristics of the NR×NT MIMO channel H, resulting in so-called channel dependent precoding. This is also commonly referred to as closed-loop precoding and essentially strives to focus the transmit energy into a subspace which is strong in the sense of conveying much of the transmitted energy to the UE. In addition, the precoder matrix may also be selected to strive for orthogonalizing the channel, meaning that after proper linear equalization at the UE, the inter-layer interference is reduced.
In closed-loop precoding, the UE transmits, based on channel measurements in the forward link (downlink), recommendations to the eNodeB of a suitable precoder to use. A single precoder that is supposed to cover a large bandwidth (wideband precoding) may be fed back. It may also be beneficial to match the frequency variations of the channel and instead feed back a frequency-selective precoding report, e.g. several precoders, one per subband. This is an example of the more general case of channel state information (CSI) feedback, which also encompasses feeding back other entities than precoders to assist the eNodeB in subsequent transmissions to the UE. Such other information may include channel quality indicators (CQIs) as well as transmission rank indicator (RI).
One problem with closed-loop precoding is the feedback overhead caused by signaling a precoder matrix indicator (PMI) and precoder rank indicator (i.e., a RI)—especially in systems with large antenna configurations where there are many channel dimensions to characterize. With the state-of-the art feedback design, the feedback overhead for systems with many transmit antennas will in many cases result in an unreasonable feedback overhead. Complexity also may be a problem if conventional feedback schemes are used as the antenna array sizes grow larger. In this regards, searching for the “best” precoder from among candidate precoder matrices in a large codebook is computationally demanding, as it essentially implies an exhaustive search over the large number of codebook entries.